Generally, entity such as organization, industries, companies, departments etc. makes use of software applications to detect bugs. The bugs may be software bugs or defects which are the common terms to describe errors, flaws, mistakes, failures, or faults in a computer program or software programming applications or systems. Such bugs produce incorrect or unexpected results. Occurrences of the bugs affect performance of one or more infrastructures of the entity. The performance of the one or more infrastructures include, without limitations, functioning of the software applications, performance of operating systems used to run the software applications, performance of servers utilized towards running of the software application, performance of one or more databases accessed for the software applications etc. and/or causes one or more infrastructures to behave in unintended ways.
Typically, in one conventional method, two sets of performance metrics of the software applications are closely monitored. The first set of performance metrics defines the performance experienced by end users of the software applications. One example of performance experienced by end users is average response time under peak load. The components of the set include, without limitations, load and response time where the ‘load’ is the volume of transactions processed by the software application and the ‘response time’ is the time required for a software application to respond to a user's actions at such a load. The second set of performance metrics measures the computational resources used by the software applications for the load. In such a case, an adequate capacity to support the load and possible locations of a performance bottleneck i.e. bugs are indicated. Further, in the conventional method, each of the metrics is consolidated and reports are generated on the performance of each application using the consolidated metrics to present the application performance data. However, the consolidation of each metric includes collection and processing of performance statistics from various tools and stake holders. In such a way, processing of the various system details consume high amount of manual efforts due to complex and large application infrastructure components. For example, two persons takes two days to generate a detail report, two persons take one day to generate a comparison report, one person takes four hours to generate a Hypertext Mark-up Language (HTML) report. Due to such difference of tasks performed by each person to generate the reports delays in generating the consolidated report. Further, maintenance of the history of performance reports for comparison between the past and present application performance is a challenge due to a huge amount of data from complex and large infrastructure components for analysis and report generation.
In one conventional method, the report is generated by retrieving data from various application components, by analyzing the reports, by comparing/correlating the various performance parameters with each other and/or with predefined parameters. In such a way, root causes to the generation of the bugs are identified. Further, the correlated results are presented in different formats based on the business requirements. However, with the complex infrastructure details, there exists a challenge to deliver the correlated reports within the compliance targets due to one or more problems. Typically, the one such problem is logging onto each and every network component, different websites, several backend infrastructure layers and collecting results from various stake holders for every test run on timely basis is a tedious process which takes huge amount of time and also depends on the stakeholder. Another such problem is analyzing the data gathered from the various infrastructure layers which is a time consuming activity and prone to human error. Another problem arises for comparing the reports for providing performance trends between different releases i.e. different versions of the software applications being used. Such problem is due to the huge amount of data, vast infrastructure and analyzing ‘n’ number of the reports for each infrastructure layer. Further, the problem includes difficulty to maintain the different repositories i.e. storages for storing all the performance results or performance data or performance statistics due to high space utilization of non-collated result files and folders.